In this work, we focus on fairness-aware stream classification in the presence of skewed class distributions. Our approach, called online fairness and class imbalance-aware boosting (\(\mathsf {FABBOO}\) ), monitors the class imbalance as well as the discriminatory behavior of the stream learner on the incoming stream and updates a boosting classifier [1]} to tackle these issues concurrently. In contrast to existing works such as [2]} that focus on short-term mitigation by considering only the behavior of the model within a data chunk, \(\mathsf {FABBOO}\)  is able to mitigate cumulative discriminatory outcomes by accounting for discrimination from the beginning of the stream up to the current timepoint. Our experiments verify that when treating for short-term discriminatory outcomes, the cumulative effects can be substantially higher over time and therefore, a cumulative approach is better. Furthermore, \(\mathsf {FABBOO}\)  can accommodate various parity-based notions of fairness, namely statistical parity, equal opportunity and predictive equality. Finally, \(\mathsf {FABBOO}\)  is able to adapt to underlying changes in the data distribution –the so-called concept drifts– by employing Adaptive Hoeffding Trees [3]} as weak learners, thus (blindly) adapting to concept drifts.
